La enseñanza de Física en una escuela militar ¿Una herencia behaviorista?

This case study consists of a classroom ethnography in which the daily basis of physics as a discipline in a classroom of students of the second year of high school, in a Brazilian military school, is described. It comprises a set of more comprehensive ethnographic studies in different types of schools as well as in the context of higher education teaching. It aims at investigating possible contributions of aspects related to the nature of science as a potentially useful strategy for the improvement of physics teaching. The observed physics teacher did not have in his schooling any formal study of epistemological disciplines, so that it was also the goal of this research the identification of his epistemological conceptions and of any feasible relationships between these conceptions and his teaching praxis. Research findings suggested a classroom reality and a school context that seemed to favor cross-examining the existence behaviorist legacy, although under a different disguise, as well as the need to reflect upon the extent to which this legacy can be considered harmful

Object-oriented Programming Laws for Annotated Java Programs

Object-oriented programming laws have been proposed in the context of languages that are not combined with a behavioral interface specification language (BISL). The strong dependence between source-code and interface specifications may cause a number of difficulties when transforming programs. In this paper we introduce a set of programming laws for object-oriented languages like Java combined with the Java Modeling Language (JML). The set of laws deals with object-oriented features taking into account their specifications. Some laws deal only with features of the specification language. These laws constitute a set of small transformations for the development of more elaborate ones like refactorings

ZIKA: A New System to Empower Health Workers and Local Communities to Improve Surveillance Protocols by E-learning and to Forecast Zika Virus in Real Time in Brazil

The devastating consequences of neonates infected with the Zika virus makes it necessary to fight and stop the spread of this virus and its vectors (Aedes mosquitoes). An essential part of the fight against mosquitoes is the use of mobile technology to support routine surveillance and risk assessment by community health workers (health agents). In addition, to improve early warning systems, the public health authorities need to forecast more accurately where an outbreak of the virus and its vector is likely to occur. The ZIKΛ system aims to develop a novel comprehensive framework that combines e-learning to empower health agents, community-based participatory surveillance, and forecasting of occurrences and distribution of the Zika virus and its vectors in real time. This system is currently being implemented in Brazil, in the cities of Campina Grande, Recife, Jaboatão dos Guararapes, and Olinda, the State of Pernambuco and Paraiba with the highest prevalence of the Zika virus disease. In this paper, we present the ZIKA system which helps health agents to learn new techniques and good practices to improve the surveillance of the virus and offer a real time distribution forecast of the virus and the vector. The forecast model is recalibrated in real time with information coming from health agents, governmental institutions, and weather stations to predict the areas with higher risk of a Zika virus outbreak in an interactive map. This mapping and alert system will help governmental institutions to make fast decisions and use their resources more efficiently to stop the spread of the Zika virus. The ZIKA app was developed and built in Ionic which allows for easy cross-platform rendering for both iOS and Android. The system presented in the current paper is one of the first systems combining public health surveillance, citizen-driven participatory reporting and weather data-based prediction. The implementation of the ZIKA system will reduce the devastating consequences of Zika virus in neonates and improve the life quality of vulnerable people in Brazil

MEWAR: Development of a Cross-Platform Mobile Application and Web Dashboard System for Real-Time Mosquito Surveillance in Northeast Brazil

Mosquito surveillance is a crucial process for understanding the population dynamics of mosquitoes, as well as implementing interventional programs for controlling and preventing the spread of mosquito-borne diseases. Environmental surveillance agents who performing routine entomological surveys at properties in areas where mosquito-borne diseases are endemic play a critical role in vector surveillance by searching and destroying mosquito hotspots as well as collate information on locations with increased infestation. Currently, the process of recording information on paper-based forms is time-consuming and painstaking due to manual effort. The introduction of mobile surveillance applications will therefore improve the process of data collection, timely reporting, and field worker performance. Digital-based surveillance is critical in reporting real-time data; indeed, the real-time capture of data with phones could be used for predictive analytical models to predict mosquito population dynamics, enabling early warning detection of hotspots and thus alerting fieldworker agents into immediate action. This paper describes the development of a cross-platform digital system for improving mosquito surveillance in Brazil. It comprises of two components: a dashboard for managers and a mobile application for health agents. The former enables managers to assign properties to health workers who then survey them for mosquitoes and to monitor the progress of inspection visits in real-time. The latter, which is primarily designed as a data collection tool, enables the environmental surveillance agents to act on their assigned tasks of recording the details of the properties at inspections by filling out digital forms built into the mobile application, as well as details relating to mosquito infestation. The system presented in this paper was co-developed with significant input with environmental agents in two Brazilian cities where it is currently being piloted

A review exploring the overarching burden of Zika virus with emphasis on epidemiological case studies from Brazil

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil

Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation

Guidelines on the diagnosis, treatment and management of visceral and renal arteries aneurysms: a joint assessment by the Italian Societies of Vascular and Endovascular Surgery (SICVE) and Medical and Interventional Radiology (SIRM)

: The objective of these Guidelines is to provide recommendations for the classification, indication, treatment and management of patients suffering from aneurysmal pathology of the visceral and renal arteries. The methodology applied was the GRADE-SIGN version, and followed the instructions of the AGREE quality of reporting checklist. Clinical questions, structured according to the PICO (Population, Intervention, Comparator, Outcome) model, were formulated, and systematic literature reviews were carried out according to them. Selected articles were evaluated through specific methodological checklists. Considered Judgments were compiled for each clinical question in which the characteristics of the body of available evidence were evaluated in order to establish recommendations. Overall, 79 clinical practice recommendations were proposed. Indications for treatment and therapeutic options were discussed for each arterial district, as well as follow-up and medical management, in both candidate patients for conservative therapy and patients who underwent treatment. The recommendations provided by these guidelines simplify and improve decision-making processes and diagnostic-therapeutic pathways of patients with visceral and renal arteries aneurysms. Their widespread use is recommended

The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects